This is a great illustration of the tensions between theory and practice in ML / AI: To some degree, this may be "just" aa matter of a poor training set: too f

This is a great illustration of the tensions between theory and practice in ML / AI: To some degree, this may be "just" aa matter of a poor training set: too few / unbalanced examples. Can't we just use a better dataset? But this also reflects a more systemic problem with descriptive / learned behavior, rather than ontological representations: we will always have these dataset problems, to be fixed after the fact when someone points out the flaw. See also facial recognition of non-white faces. We want intelligent systems that can learn from the world, but that's not an accurate mental model of how humans learn. Often, we learn our values and develop opinions in spite of empirical representations—I don't read the newspaper to find out what Muslim people are like, I start from my values / convictions (all men are created equal, every person deserves respect and the benefit of the doubt prima facie) and maybe update on the basis of observations of media depictions. Yes, a computer may be able to imitate human speech after reading a lot of text...but that doesn't mean it understands the content of that text.